RETRACTED: Applying TRIZ and Fuzzy AHP to develop innovative design for automated manufacturing systems

Li, Te-Sheng; Huang, Hsing-Hsin

doi:10.1016/j.eswa.2008.10.025

Cited by 90 publications

(37 citation statements)

References 20 publications

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“…The presented decision tool enhances and upgrades preceded AHP implementation for prototyping selection, [34,36,41], both in terms of the considered decision criteria and of the prototype realization alternatives considered, in a realistic way and in close contact with the current industrial practice.…”

Section: Model Structure and Methodsmentioning

confidence: 99%

Rational Embracing of Modern Prototyping Capable Design Technologies into the Tools Pool of Product Design Teams

Polydoras

Sfantsikopoulos

Provatidis

2011

ISRN Mechanical Engineering

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Today's markets impose wide sets of requirements for new products. The Process of New Product Design, has shifted from sequential Engineering to Concurrent Engineering, for low cost-early market entry. Design Teams must deliver highest quality within minimum time and cost and global crisis worsens things, by further reducing budgets. Prototyping within design processes has become crucial, whereas, available prototyping alternatives have increased. Decisions regarding the use of the most appropriate one, at specific design milestones, affects and much predetermines the success of the product. This paper addresses the problem of "design target"-based embracing of available prototyping alternatives into the tools pool of design teams, in a concise, integrated way. Considering product design teams are expert driven for specific product categories, the introduced approach records the verification intent of designers and binds it to structured Generic Levels of Technical and non-Technical Attribute clusters and associated Design Factors. Furthermore, prototyping experience, manufacturing capabilities and cost of the implementing organization, local industrial status and regulations, are also considered. Utilizing the Analytic Hierarchy Process a complete decision tree leads designers to select the most appropriate prototyping method per design stage. The proposed approach assists implementing organizations and design teams towards cost/time benefits, product risk reduction, decision repeatability and independence. A pilot-model has been developed with "Expert Choice" software and an application example is discussed.

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Section: Model Structure and Methodsmentioning

confidence: 99%

Rational Embracing of Modern Prototyping Capable Design Technologies into the Tools Pool of Product Design Teams

Polydoras

Sfantsikopoulos

Provatidis

2011

ISRN Mechanical Engineering

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“…It showed how to decide the relative importance of activities in a multi attribute decision making problem. It is one of the widely used decision making approach in industrial problems (Li & Huang, 2009). The AHP is built on a human inherent capability to construct its views hierarchically.…”

Section: Analytical Hierarchy Process (Ahp)mentioning

confidence: 99%

Synergy of fuzzy AHP and Six Sigma for capacity waste management in Indian automotive industry

Rathi¹,

Khanduja²,

Sharma³

2015

10.5267/j.dsl

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Capacity waste management is highly essential because under utilization of capacity is often referred to as a major reason for lower productivity among industries around the world. For better estimation of capacity and its utilization and then for its improved management; newer techniques are being devised in industrial sector. The current case of capacity waste problem has been taken up as a Six Sigma project, where we try to analyze critical factors responsible for the capacity waste. Decisions on critical factor selection in analysis phase of Six Sigma are always very crucial. The paper discusses an approach for selection of capacity waste factors at an automotive industry using fuzzy logic based AHP method. The fuzzy AHP is a well recognized tool to undertake the fuzziness of the data involved in choosing the preferences of the different decision variables engaged in the process of capacity waste factors selection. In this context, we have explored six crucial parameters for selection of capacity waste factors. Final ranking is calculated through priority vector thus obtained and it is seen that conveyor malfunction is found to be the key factor for capacity waste among all alternatives at the selected site.ed. Growing Science Ltd. All rights reserv 5 © 201

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“…Fuzzy set theory includes the fuzzy logic, fuzzy arithmetic, fuzzy mathematical programming, fuzzy graph theory and fuzzy data analysis, usually the term fuzzy logic is used to describe all of these (Kahraman et al, 2004;Chan & Kumar, 2007). Fuzzy set theory and it has been applied in a variety of fields in the last decades (Li & Huang, 2009). The main difference between fuzzy sets and crisp sets is that crisp sets only allow full membership or non-membership at all, whereas fuzzy sets allow partial membership (Balli & Korukoğlu, 2009).…”

Section: Fuzzy Sets Theorymentioning

confidence: 99%

An order acceptance using FAHP and TOPSIS methods: A case study of Iranian vehicle belt production industry

Parsaei¹,

Keramati²,

Zorriassatine³

et al. 2012

ijiec

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Decisions related to acceptance or rejection of orders play an important role in companies engaged in make-to-order production. The incoming orders have a specific delivery date by which the customer expects the due date to be met and the order delivered. In some cases the level of input orders exceeds beyond the existing capacity. In such situations the main concern is to decide which orders must be accepted and which ones rejected taking into account the available production capacity. This paper prioritises the input orders according to a comprehensive and systematic multi criteria decision making (MCDM) model. It then proceeds with making decisions to either accept or reject orders according to the calculated prioritises and production constraints. Ultimately the optimum list of orders for acceptance is determined. The proposed model is a combination of two techniques of Fuzzy Analytical Hierarchy Process (FAHP) and Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS). In this model FAHP is used to determine the weights of criteria and TOPSIS is used for prioritizing the orders. Finally the proposed model is tested for its efficiency by application to a real case.

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RETRACTED: Applying TRIZ and Fuzzy AHP to develop innovative design for automated manufacturing systems

Cited by 90 publications

References 20 publications

Rational Embracing of Modern Prototyping Capable Design Technologies into the Tools Pool of Product Design Teams

Rational Embracing of Modern Prototyping Capable Design Technologies into the Tools Pool of Product Design Teams

Synergy of fuzzy AHP and Six Sigma for capacity waste management in Indian automotive industry

An order acceptance using FAHP and TOPSIS methods: A case study of Iranian vehicle belt production industry

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